1. Field of Invention
Claimed embodiments of the invention relate to oil and gas reservoir modeling and more particularly to computer-implemented methods, systems, and non-transitory computer-readable medium having one or more computer programs stored therein to model a core sample or other rock sample.
2. Background of the Invention
A reservoir can be digitally modeled so as to reflect all of the reservoir's characteristics related to its ability to store and produce hydrocarbons. Once completed, a reservoir model can be used to run flow simulations to predict, for example, residual oil saturations or recovery factors. These models, also called simulations, may be static or dynamic. Static models are fine-scale reservoir models of rock properties such as, for example, porosity, permeability, capillary pressure, fractures, faults, seismic attributes, and parameters that do not change significantly with time. Dynamic models, on the other hand, are coarser models. They incorporate fluid dynamic properties that change with time, such as, for example, pressure and flow rates of hydrocarbons and water. Static models are sometimes called reservoir-description grids or simply geological models, while dynamic models are sometimes called simulation grids.
Reservoir models may vary in scale from one another by as many as twelve orders of magnitude. For instance, one model might include pores, which can be measured in nanometers, while another model might represent a full oil field, which can be measured in kilometers. Highly-detailed models are often unsuitable for simulations. Consequently, detailed models are sometimes “scaled up” or “coarsened;” this process is referred to as “upscaling.” Upscaling is accomplished through the use of various algorithms. After upscaling has occurred, detailed features are no longer represented, but broader characteristics are still represented in the model.
Reservoir models can be developed in a variety of ways and from a variety of data sources. For example, some models are developed from borehole images. Borehole images can be acquired through several techniques. One such technique is to use electrode pads placed against the wellbore wall around the wellbore to force a current through the rock; sensors can then measure the current and map resistivities of the material surrounding the wellbore. The readings can then be used to develop an image of the material and features, e.g., vugs, that make up the sampled portions of the wellbore wall. Statistical techniques such as multipoint statistics (MPS) can be used to model the full wellbore wall, including gaps between the sampled images. MPS employs “training images” as templates in modeling reservoir properties. Training images can be existing geological interpretations, but they do not need to be.
Another method of modeling a reservoir is the numerical pseudocores method. In that method, numerical pseudocores are three-dimensional models derived from both borehole images and digital rock samples. The method utilizes MPS, and the digital rock samples serve as training images.
Digital rock samples—digital representations of core samples or other rock samples—can be constructed from image sets obtained by, for example, x-ray computed tomography (CT) scan (CTS), micro-CT scan, or confocal microscopy. To obtain image sets by CTS, an x-ray is passed through two-dimensional transverse sections of a core sample from all sides of the sample, and density is calculated. Micro-CT scan similarly uses x-ray technology to obtain images. Confocal microscopy uses fluorescence properties of different materials to create images. After “scanning” a number of transverse sections of the core sample, a three-dimensional model of the sample can be constructed. The model will show the rock and its features, e.g., vugs.
To construct a model of a core sample, the scanned images of transverse sections of the core sample must often be manipulated. The process of manipulating the images, however, frequently requires subjective decision-making and variable adjustments by individuals. These factors cause the process of constructing a model to be time-consuming and result in models that are often prone to inaccuracies.